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Self-supervised learning in remote sensing: A review
In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …
triggering interest within both the computer vision and remote sensing communities. While …
Vision-language models in remote sensing: Current progress and future trends
The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …
4) have sparked a wave of interest and research in the field of large language models …
RingMo: A remote sensing foundation model with masked image modeling
Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …
A billion-scale foundation model for remote sensing images
As the potential of foundation models in visual tasks has garnered significant attention,
pretraining these models before downstream tasks has become a crucial step. The three key …
pretraining these models before downstream tasks has become a crucial step. The three key …
Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …
sensing images (RSIs). To better understand the connection between three feature learning …
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Research in self-supervised learning (SSL) with natural images has progressed rapidly in
recent years and is now increasingly being applied to and benchmarked with datasets …
recent years and is now increasingly being applied to and benchmarked with datasets …
Beyond supervised learning in remote sensing: A systematic review of deep learning approaches
An increasing availability of remote sensing data in the era of geo big-data makes producing
well-represented, reliable training data to be more challenging and requires an excessive …
well-represented, reliable training data to be more challenging and requires an excessive …
Self-supervised multimodal learning: A survey
Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …
modalities, has achieved substantial progress in the supervised regime in recent years …
Self-supervised pretraining via multimodality images with transformer for change detection
Self-supervised learning (SSL) has shown remarkable success in image representation
learning. Among these methods, masked image modeling and contrastive learning are the …
learning. Among these methods, masked image modeling and contrastive learning are the …
Deep unsupervised contrastive hashing for large-scale cross-modal text-image retrieval in remote sensing
Due to the availability of large-scale multi-modal data (eg, satellite images acquired by
different sensors, text sentences, etc) archives, the development of cross-modal retrieval …
different sensors, text sentences, etc) archives, the development of cross-modal retrieval …